Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach-Reference-Cited by-同舟云学术

Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach

Published:2024 Issue: Volume: Page:239-253
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:en
Short-container-title:

Author:

Teles Felipe Rogério Silva^ORCID,Mendes Alison Corrêa^ORCID,Paiva Anselmo Cardoso de^ORCID,Almeida João Dallyson Sousa de^ORCID,Junior Geraldo Braz^ORCID,Silva Aristófanes Corrêa^ORCID,Neto Pedro De Alcantara Dos Santos^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-60665-6_18

Reference33 articles.

1. Abdi, A.H., Kasaei, S., Mehdizadeh, M.: Automatic segmentation of mandible in panoramic x-ray. J. Med. Imaging 2(4), 044003–044003 (2015)

2. Al-Sarem, M., Al-Asali, M., Alqutaibi, A.Y., Saeed, F.: Enhanced tooth region detection using pretrained deep learning models. Int. J. Environ. Res. Public Health 19(22), 15414 (2022)

3. Alam, M.K., et al.: Teeth segmentation by optical radiographic images using VGG-16 deep learning convolution architecture with R-CNN network approach for biomedical sensing applications. Opt. Quant. Electron. 55(9), 808 (2023)

4. Almalki, A., Latecki, L.J.: Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 5594–5603 (2023)

5. Astuti, E.R., et al.: The sensitivity and specificity of YOLO v4 for tooth detection on panoramic radiographs. J. Int. Dent. Med. Res. 16(1), 442–446 (2023)